Medical imaging has been an expanding field for several decades. With increasing diagnostic tools, increasing population, more wide-spread access to medical treatment, and the desirability of sharing information between doctors and professionals, medical imaging is likely to continue growing. To address this continued growth, and the subsequent inconveniences of paper and other fixed forms of medical image storage, the medical community has increasingly turned to digital forms of image storage.
Picture Archiving and Communications Systems (PACS) are a common example of a digital image system. These systems connect elements such as imaging modalities, storage databases or areas, clients that use the medical image data, and data processing devices to create a network of imaging technology. Such systems then provide easier remote diagnosis and data access, sharing of information between health-care professionals, and ultimately a better health-care system.
Currently, large volume Computed Tomography (CT), Magnetic Resonance (MR) and X-Ray Angiography (XA) DICOM studies utilized by local PACS networks pose a significant image data management problem due to the large number of image data files that must be transferred within and between local PACS networks. A typical image dataset can easily contain over 2000 slices that translates into a similar number of image data files.
Each image data file can be expressed as two logical parts. One part is the meta-data that represents a set of attributes that describes the image. The other part is known as pixel data that represents the displayed image. Each image data file in an image dataset contains a significant amount of common meta-data such as patient, study, and image attributes. Since each individual image data file contains its own common meta-data, this results in a substantial amount of redundant meta-data being stored in the image display caches and being mapped into the database, as is commonly the case within local PACS networks.
To address these issues, a new Digital Imaging and Communications in Medicine (DICOM) image data standard has been developed which defines a new multi-frame DICOM image data object into which any number of CT, MR or XA slices can be combined. These new multi-frame DICOM image data objects are called Enhanced Multi-Frame image data objects. The term multi-frame simply refers to a DICOM image data object that contains a number of distinct but related images in a single file. This object, similar to the conventional single frame object contains both meta-data and pixel data. However, meta-data, in the case of the new multi-frame DICOM image data object, is combined, grouped by shared attributes. This approach removes the inefficiency of the redundancy and significantly decreases the storage required for image data object meta-data.
However, the adoption rate of the new DICOM image data standard by CT, MR or XA imaging modality vendors has been slow. In addition, the number of existing large volume CT, MR, or XA equipment installed within medical facilities such as hospitals is substantial. Since the cost of CT, MR and XA imaging modalities is high, the rate at which hospitals upgrade these systems is also slow. Given this, support for the new Enhanced Multi-Frame image data objects will also be slow. Finally, and potentially more importantly, large volumes of image data objects that were created using the old DICOM data standard will be stored within PACS systems for many years, even after the old data standard imaging modalities are upgraded to the new Enhanced Multi-Frame standard. Consequently, large volume image data studies stored as individual slices will be commonplace for some time with the associated inefficiencies.